SUPERVISOR: Wolfram GRAF

PROJECT ASSIGNED TO: Aine AMON

Freshwater ecosystems in Uganda are rapidly deteriorating due to pressure to sustain the livelihood of a fast-growing population. The knowledge gaps on ecosystems still hinder empirical decision making for integrated sustainable management and utilization of these systems. Management decisions are still solely based on analysis of water physicochemical parameters which are expensive for large scale projects and poor at inferring historical and general ecological perspective. Integration of bioassessment tools into freshwater assessment frameworks have been futile due to limited systematic protocols and baseline information on the natural spatial and temporal ecological dynamic. This study will fill a knowledge gap on the foundational components of Uganda’s river ecosystems important to establish a biomonitoring and bioassessment system based on macroinvertebrates for Ugandan aquatic ecosystems. The study will explore and delineate ecological zones, characterise major river types and assess the impact of major stressors using benthic macroinvertebrates as proxies of ecosystem diversity and functionality. Findings will be up scalable for development of bioassessment frameworks and policy at national level. To achieve the above objectives, three hypotheses will be tested: 

  1. the climatic, geological, hydro morphological and ecological characteristics of rivers naturally vary at the local, catchment and regional scale and therefore they can be classified into distinct ecological zones and a river typology that maximise homogeneity at a given spatial scale. 

  2. (ii) Biota are highly diverse, however they have specific habitat (climatic, geological and topological) requirements at local, regional and national scales, therefore, the composition and structure of potential natural vegetation of Uganda will be more similar within a given ecozone than between ecozones. 

  3. (iii) Macroinvertebrate taxa are highly diverse (in morphological and functional adaptation) with specific habitat requirements and therefore, the composition and structure of biotic communities will be more similar within a given ecozone than between ecozones.

Existing national and global secondary data sets of climatic, topological, vegetation and geological data sets will be obtained and explored objective delineation of ecozones using machine learning algorithms. Representative rivers will be selected and sampled for benthic macroinvertebrates following the EU AQEM, 2002 protocol while Standard Method of Water Quality examination will be used for abiotic parameters. Abiotic parameters will be subjected to cluster analysis tests, optimized using biota homogeneity tests and relationships determined using regression and dimension reduction techniques. Multivariate analysis tests will be used to analyse for sensitivity and indicator taxa.